ronith128/malware-analysis

1import gradio as gr2import numpy as np3from tensorflow.keras.models import load_model4from tensorflow.keras.preprocessing import image5import tensorflow as tf6from PIL import Image7import os8 9model = load_model('model.h5')10 11def convertToImage(file):12    with open(file, "rb") as f:13        byte = f.read(1)  # reading 1 byte at a time. returns a byte object.14        i, j, p = 0, 0, 015        image = np.zeros((256, 256))16        temp1 = None  # Define temp1 before using it17        while byte:18            try:19                decoded_byte = byte.decode("utf-8")20                if (21                    decoded_byte == " "22                    or decoded_byte == "\r"23                    or decoded_byte == "\n"24                    or decoded_byte == "?"25                ):26                    byte = f.read(1)27                    continue28                if j > 8:29                    try:30                        image[i][p] = int(decoded_byte, 16)31                        p += 132                    except ValueError:33                        pass34                j += 135                if p > 255:36                    p = 037                    j = 038                    if i < 255:39                        i += 140                        continue41                byte = f.read(1)42            except UnicodeDecodeError:43                byte = f.read(1)44                continue45        a = np.matrix(image)46        temp1 = a47        img = Image.fromarray(image, "L")48        img.save("a.bmp")49        50def predict_gender(img):51    pathe = os.path.splitext(img.name)52    extension = pathe[1]53    filename = pathe[0] + ".bmp"54    if(not extension == ".bmp"):55        convertToImage(img.name)56        filename = "a.bmp"57        58    img = image.load_img(filename, target_size=(256, 256))59    x = image.img_to_array(img)60    x = np.expand_dims(x, axis=0)61    x = x / 255.062 63    preds = model.predict(x)64 65    if preds[0][0] > 0.5:66        return "Predicted malware"67    else:68        return "Predicted benign"69 70# Create an interface with a file input and a text output71interface = gr.Interface(72    fn=predict_gender,73    inputs=gr.inputs.File(label="Upload a file"),74    outputs="text"75)76 77# Launch the interface78interface.launch()